Overview

Dataset statistics

Number of variables16
Number of observations3241
Missing cells0
Missing cells (%)0.0%
Duplicate rows257
Duplicate rows (%)7.9%
Total size in memory405.2 KiB
Average record size in memory128.0 B

Variable types

NUM13
CAT2
BOOL1

Warnings

Dataset has 257 (7.9%) duplicate rows Duplicates
Duration is highly skewed (γ1 = 32.04683548) Skewed
TempDist has 2556 (78.9%) zeros Zeros
SpatDist has 2597 (80.1%) zeros Zeros
AnzGesperrtFs has 1020 (31.5%) zeros Zeros
Length has 913 (28.2%) zeros Zeros
Duration has 504 (15.6%) zeros Zeros

Reproduction

Analysis started2020-10-24 10:57:26.448227
Analysis finished2020-10-24 10:57:52.359792
Duration25.91 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

TempMax
Real number (ℝ≥0)

Distinct208
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.0074051
Minimum9
Maximum1326
Zeros0
Zeros (%)0.0%
Memory size25.3 KiB
2020-10-24T12:57:52.425535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15
Q142
median111
Q3219
95-th percentile654
Maximum1326
Range1317
Interquartile range (IQR)177

Descriptive statistics

Standard deviation222.3546091
Coefficient of variation (CV)1.195407295
Kurtosis7.775615228
Mean186.0074051
Median Absolute Deviation (MAD)78
Skewness2.526191522
Sum602850
Variance49441.57217
MonotocityNot monotonic
2020-10-24T12:57:52.572136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
151554.8%
 
361153.5%
 
21832.6%
 
189792.4%
 
24732.3%
 
18682.1%
 
60621.9%
 
48601.9%
 
30541.7%
 
57501.5%
 
Other values (198)244275.3%
 
ValueCountFrequency (%) 
9431.3%
 
12421.3%
 
151554.8%
 
18682.1%
 
21832.6%
 
ValueCountFrequency (%) 
132670.2%
 
1323230.7%
 
132020.1%
 
11941< 0.1%
 
1116120.4%
 

TempAvg
Real number (ℝ≥0)

Distinct258
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.87442147
Minimum3
Maximum1326
Zeros0
Zeros (%)0.0%
Memory size25.3 KiB
2020-10-24T12:57:52.713365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q120
median47
Q3104
95-th percentile261
Maximum1326
Range1323
Interquartile range (IQR)84

Descriptive statistics

Standard deviation105.0379559
Coefficient of variation (CV)1.267435164
Kurtosis28.43934566
Mean82.87442147
Median Absolute Deviation (MAD)32
Skewness4.029544586
Sum268596
Variance11032.97219
MonotocityNot monotonic
2020-10-24T12:57:52.852463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
151554.8%
 
162692.1%
 
31682.1%
 
18611.9%
 
30581.8%
 
17581.8%
 
9541.7%
 
12541.7%
 
21501.5%
 
6491.5%
 
Other values (248)256579.1%
 
ValueCountFrequency (%) 
330.1%
 
440.1%
 
5240.7%
 
6491.5%
 
7451.4%
 
ValueCountFrequency (%) 
132630.1%
 
96620.1%
 
9551< 0.1%
 
85820.1%
 
7931< 0.1%
 

SpatMax
Real number (ℝ≥0)

Distinct1236
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11142.49892
Minimum699
Maximum220501
Zeros0
Zeros (%)0.0%
Memory size25.3 KiB
2020-10-24T12:57:53.000926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum699
5-th percentile1510
Q12831
median5528
Q312219
95-th percentile31880
Maximum220501
Range219802
Interquartile range (IQR)9388

Descriptive statistics

Standard deviation22009.93478
Coefficient of variation (CV)1.97531406
Kurtosis58.64762205
Mean11142.49892
Median Absolute Deviation (MAD)3257
Skewness7.104555233
Sum36112839
Variance484437229.1
MonotocityNot monotonic
2020-10-24T12:57:53.146543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
28311133.5%
 
1926642.0%
 
1014391.2%
 
2908341.0%
 
2475260.8%
 
8459250.8%
 
3306240.7%
 
2226190.6%
 
9045190.6%
 
1903170.5%
 
Other values (1226)286188.3%
 
ValueCountFrequency (%) 
6991< 0.1%
 
9021< 0.1%
 
9511< 0.1%
 
9651< 0.1%
 
99120.1%
 
ValueCountFrequency (%) 
22050170.2%
 
21908280.2%
 
189730170.5%
 
12590950.2%
 
6601120.1%
 

SpatAvg
Real number (ℝ≥0)

Distinct1326
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3281.809627
Minimum284
Maximum15602
Zeros0
Zeros (%)0.0%
Memory size25.3 KiB
2020-10-24T12:57:53.290325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum284
5-th percentile862
Q11536
median2282
Q33951
95-th percentile9929
Maximum15602
Range15318
Interquartile range (IQR)2415

Descriptive statistics

Standard deviation2778.984024
Coefficient of variation (CV)0.8467840429
Kurtosis3.870895073
Mean3281.809627
Median Absolute Deviation (MAD)985
Skewness1.968910061
Sum10636345
Variance7722752.204
MonotocityNot monotonic
2020-10-24T12:57:53.425543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15361133.5%
 
1575642.0%
 
809391.2%
 
2691240.7%
 
1276230.7%
 
1683200.6%
 
1272190.6%
 
2469190.6%
 
10266170.5%
 
4430140.4%
 
Other values (1316)288989.1%
 
ValueCountFrequency (%) 
28420.1%
 
30530.1%
 
3551< 0.1%
 
4041< 0.1%
 
4191< 0.1%
 
ValueCountFrequency (%) 
1560240.1%
 
1559040.1%
 
1505440.1%
 
1478530.1%
 
1477630.1%
 

TempDist
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.529157667
Minimum0
Maximum24
Zeros2556
Zeros (%)78.9%
Memory size25.3 KiB
2020-10-24T12:57:53.558711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.747503339
Coefficient of variation (CV)2.27249705
Kurtosis3.970395034
Mean2.529157667
Median Absolute Deviation (MAD)0
Skewness2.269746513
Sum8197
Variance33.03379463
MonotocityNot monotonic
2020-10-24T12:57:53.775426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
0255678.9%
 
7401.2%
 
14391.2%
 
3391.2%
 
9361.1%
 
6361.1%
 
10351.1%
 
8321.0%
 
12321.0%
 
18311.0%
 
Other values (15)36511.3%
 
ValueCountFrequency (%) 
0255678.9%
 
1210.6%
 
2290.9%
 
3391.2%
 
4220.7%
 
ValueCountFrequency (%) 
24220.7%
 
23130.4%
 
22290.9%
 
21270.8%
 
20240.7%
 

SpatDist
Real number (ℝ≥0)

ZEROS

Distinct432
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.3248997
Minimum0
Maximum1988
Zeros2597
Zeros (%)80.1%
Memory size25.3 KiB
2020-10-24T12:57:53.902937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1271
Maximum1988
Range1988
Interquartile range (IQR)0

Descriptive statistics

Standard deviation407.8321796
Coefficient of variation (CV)2.642685531
Kurtosis7.531771897
Mean154.3248997
Median Absolute Deviation (MAD)0
Skewness2.880401915
Sum500167
Variance166327.0867
MonotocityNot monotonic
2020-10-24T12:57:54.047525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0259780.1%
 
576431.3%
 
92381.2%
 
1025230.7%
 
198570.2%
 
43160.2%
 
150.2%
 
12040.1%
 
198640.1%
 
137830.1%
 
Other values (422)51115.8%
 
ValueCountFrequency (%) 
0259780.1%
 
150.2%
 
230.1%
 
320.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
19881< 0.1%
 
198640.1%
 
198570.2%
 
198330.1%
 
19821< 0.1%
 

Coverage
Real number (ℝ≥0)

Distinct97
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.25485961
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size25.3 KiB
2020-10-24T12:57:54.199293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q127
median43
Q362
95-th percentile85
Maximum100
Range99
Interquartile range (IQR)35

Descriptive statistics

Standard deviation23.13043721
Coefficient of variation (CV)0.5111149919
Kurtosis-0.6209844288
Mean45.25485961
Median Absolute Deviation (MAD)17
Skewness0.3981479989
Sum146671
Variance535.0171253
MonotocityNot monotonic
2020-10-24T12:57:54.350636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
481524.7%
 
35872.7%
 
27832.6%
 
85802.5%
 
100692.1%
 
32672.1%
 
64631.9%
 
47621.9%
 
34581.8%
 
25581.8%
 
Other values (87)246276.0%
 
ValueCountFrequency (%) 
170.2%
 
3100.3%
 
480.2%
 
5170.5%
 
6120.4%
 
ValueCountFrequency (%) 
100692.1%
 
991< 0.1%
 
9740.1%
 
9570.2%
 
9490.3%
 

TLCar
Real number (ℝ≥0)

Distinct772
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.674175
Minimum1000
Maximum1999
Zeros0
Zeros (%)0.0%
Memory size25.3 KiB
2020-10-24T12:57:54.498130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1048
Q11271
median1521
Q31751
95-th percentile1938
Maximum1999
Range999
Interquartile range (IQR)480

Descriptive statistics

Standard deviation283.6809871
Coefficient of variation (CV)0.188784097
Kurtosis-1.177110507
Mean1502.674175
Median Absolute Deviation (MAD)237
Skewness-0.06217156351
Sum4870167
Variance80474.90245
MonotocityNot monotonic
2020-10-24T12:57:54.646432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15551093.4%
 
1297652.0%
 
1286391.2%
 
1171280.9%
 
1152280.9%
 
1518250.8%
 
1788200.6%
 
1015200.6%
 
1667200.6%
 
1887180.6%
 
Other values (762)286988.5%
 
ValueCountFrequency (%) 
100030.1%
 
1001100.3%
 
100350.2%
 
100430.1%
 
100550.2%
 
ValueCountFrequency (%) 
199920.1%
 
199740.1%
 
199660.2%
 
199570.2%
 
19941< 0.1%
 

TLHGV
Real number (ℝ≥0)

Distinct475
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.9500154
Minimum500
Maximum999
Zeros0
Zeros (%)0.0%
Memory size25.3 KiB
2020-10-24T12:57:54.789550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile513
Q1609
median734
Q3847
95-th percentile969
Maximum999
Range499
Interquartile range (IQR)238

Descriptive statistics

Standard deviation144.4803822
Coefficient of variation (CV)0.1976610974
Kurtosis-1.130924543
Mean730.9500154
Median Absolute Deviation (MAD)122
Skewness0.1414501679
Sum2369009
Variance20874.58083
MonotocityNot monotonic
2020-10-24T12:57:54.934388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7871233.8%
 
505682.1%
 
612451.4%
 
738351.1%
 
737351.1%
 
513311.0%
 
571311.0%
 
579260.8%
 
987220.7%
 
997210.6%
 
Other values (465)280486.5%
 
ValueCountFrequency (%) 
50090.3%
 
50160.2%
 
502100.3%
 
50320.1%
 
50440.1%
 
ValueCountFrequency (%) 
99960.2%
 
99820.1%
 
997210.6%
 
99670.2%
 
99560.2%
 

Strasse
Categorical

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
A 3
1075 
A 9
656 
A 99
312 
A 7
302 
A 96
230 
Other values (12)
666 
ValueCountFrequency (%) 
A 3107533.2%
 
A 965620.2%
 
A 993129.6%
 
A 73029.3%
 
A 962307.1%
 
A 61986.1%
 
A 931604.9%
 
A 73862.7%
 
A 92822.5%
 
A 94561.7%
 
Other values (7)842.6%
 
2020-10-24T12:57:55.083140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T12:57:55.203889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length3.316877507
Min length3

AnzGesperrtFs
Real number (ℝ)

ZEROS

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6772601049
Minimum-1
Maximum3
Zeros1020
Zeros (%)31.5%
Memory size25.3 KiB
2020-10-24T12:57:55.304688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum3
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.490783598
Coefficient of variation (CV)0.7246604287
Kurtosis-0.4154722493
Mean0.6772601049
Median Absolute Deviation (MAD)0
Skewness-0.7561438611
Sum2195
Variance0.24086854
MonotocityNot monotonic
2020-10-24T12:57:55.398180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
1218967.5%
 
0102031.5%
 
-1200.6%
 
2100.3%
 
320.1%
 
ValueCountFrequency (%) 
-1200.6%
 
0102031.5%
 
1218967.5%
 
2100.3%
 
320.1%
 
ValueCountFrequency (%) 
320.1%
 
2100.3%
 
1218967.5%
 
0102031.5%
 
-1200.6%
 

Einzug
Real number (ℝ≥0)

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.57729096
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size25.3 KiB
2020-10-24T12:57:55.504468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.648689024
Coefficient of variation (CV)0.6396984469
Kurtosis-1.309767634
Mean2.57729096
Median Absolute Deviation (MAD)1
Skewness0.6511708881
Sum8353
Variance2.718175498
MonotocityNot monotonic
2020-10-24T12:57:55.605341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
2117336.2%
 
1107633.2%
 
597730.1%
 
3140.4%
 
41< 0.1%
 
ValueCountFrequency (%) 
1107633.2%
 
2117336.2%
 
3140.4%
 
41< 0.1%
 
597730.1%
 
ValueCountFrequency (%) 
597730.1%
 
41< 0.1%
 
3140.4%
 
2117336.2%
 
1107633.2%
 

Richtung
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
1
3171 
0
 
70
ValueCountFrequency (%) 
1317197.8%
 
0702.2%
 
2020-10-24T12:57:55.690245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length
Real number (ℝ≥0)

ZEROS

Distinct1425
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean950.953718
Minimum0
Maximum24500
Zeros913
Zeros (%)28.2%
Memory size25.3 KiB
2020-10-24T12:57:55.774533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median300
Q31244
95-th percentile3976
Maximum24500
Range24500
Interquartile range (IQR)1244

Descriptive statistics

Standard deviation1684.207434
Coefficient of variation (CV)1.771071927
Kurtosis33.86387851
Mean950.953718
Median Absolute Deviation (MAD)300
Skewness4.466382517
Sum3082041
Variance2836554.68
MonotocityNot monotonic
2020-10-24T12:57:56.029971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
091328.2%
 
100190.6%
 
150130.4%
 
200130.4%
 
300100.3%
 
50590.3%
 
6590.3%
 
40090.3%
 
50090.3%
 
6680.2%
 
Other values (1415)222968.8%
 
ValueCountFrequency (%) 
091328.2%
 
81< 0.1%
 
101< 0.1%
 
2620.1%
 
2720.1%
 
ValueCountFrequency (%) 
245001< 0.1%
 
210321< 0.1%
 
174301< 0.1%
 
168201< 0.1%
 
141851< 0.1%
 

Duration
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct507
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.4566492
Minimum0
Maximum187650
Zeros504
Zeros (%)15.6%
Memory size25.3 KiB
2020-10-24T12:57:56.173337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median27
Q392
95-th percentile530
Maximum187650
Range187650
Interquartile range (IQR)88

Descriptive statistics

Standard deviation4506.015147
Coefficient of variation (CV)14.10524764
Kurtosis1175.267134
Mean319.4566492
Median Absolute Deviation (MAD)27
Skewness32.04683548
Sum1035359
Variance20304172.5
MonotocityNot monotonic
2020-10-24T12:57:56.312733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
050415.6%
 
11153.5%
 
4922.8%
 
2872.7%
 
3862.7%
 
5561.7%
 
7541.7%
 
9501.5%
 
12421.3%
 
14401.2%
 
Other values (497)211565.3%
 
ValueCountFrequency (%) 
050415.6%
 
11153.5%
 
2872.7%
 
3862.7%
 
4922.8%
 
ValueCountFrequency (%) 
1876501< 0.1%
 
1320601< 0.1%
 
753301< 0.1%
 
391801< 0.1%
 
3213020.1%
 

Month
Categorical

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
Jul
551 
Sep
370 
Oct
307 
Dec
278 
May
276 
Other values (7)
1459 
ValueCountFrequency (%) 
Jul55117.0%
 
Sep37011.4%
 
Oct3079.5%
 
Dec2788.6%
 
May2768.5%
 
Aug2738.4%
 
Apr2718.4%
 
Mar2166.7%
 
Nov2136.6%
 
Jun2086.4%
 
Other values (2)2788.6%
 
2020-10-24T12:57:56.453288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T12:57:56.566215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-10-24T12:57:29.364331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:29.591021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:29.716141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:29.826904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:29.948402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:30.065384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:30.186919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:30.299914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:30.414125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:30.531573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:30.669798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:30.792115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:30.907463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:31.031253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:31.144910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:31.261343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:31.371173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:31.495756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:31.612203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:31.732526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:31.850269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:31.967456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:32.082464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:32.203467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:32.325356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:32.438490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:32.564554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:32.673212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:32.779746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:32.992738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:33.133014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:33.252533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:33.373781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:33.478969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:33.587360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:33.702904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:33.818287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:33.932779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:34.044244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:34.170011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:34.309294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:34.453460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:34.591254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:34.737289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:34.864011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:34.993884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:35.123614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:35.245861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:35.370570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:35.507211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:35.659746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:35.801927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:35.948305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:36.077553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:36.204367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:36.325135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:36.454707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:36.684531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:36.817299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:36.936280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:37.062750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:37.185864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:37.304521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:37.438826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:37.567754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:37.686867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:37.809508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:37.934177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:38.054880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:38.202815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:38.338402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:38.481435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:38.614465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:38.753187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:38.892583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:39.036675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:39.182114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:39.309984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:39.449316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:39.578656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:39.705432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:39.843893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:39.979594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:40.099573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:40.335776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:40.458708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:40.574666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:40.708375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:40.863267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:41.017404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:41.170658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:41.316859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:41.459951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:41.623673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:41.787722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:41.936184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:42.082947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:42.221726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:42.360539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:42.478564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:42.653577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:42.815537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:42.952274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:43.075724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:43.218423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:43.355294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:43.475983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:43.590226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:43.712391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:43.826767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:43.959291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:44.083540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:44.309442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:44.436252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:44.571841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:44.705348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:44.815840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:44.932321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:45.054519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:45.185320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:45.303675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:45.435008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:45.561301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:45.707663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:45.844057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:45.978674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:46.102358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:46.236129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:46.367047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:46.488952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:46.624629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:46.750611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:46.884946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:47.019560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:47.166514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:47.293971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:47.427698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:47.553258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:47.685659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:47.916901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:48.068745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:48.215958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:48.354611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:48.483256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:48.599555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:48.712146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:48.816478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:48.938175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:49.052956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:49.170077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:49.284055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:49.411668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:49.536308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:49.669306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:49.790523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:49.896947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:50.013878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:50.134651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:50.256702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:50.369275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:50.498389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:50.631663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:50.775963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:50.896452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:51.017689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:51.135244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:51.366762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:51.502877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:51.622545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-24T12:57:56.680974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-24T12:57:56.922684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-24T12:57:57.137389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-24T12:57:57.355872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-24T12:57:57.550695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-24T12:57:51.888417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T12:57:52.227750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

TempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseAnzGesperrtFsEinzugRichtungLengthDurationMonth
0693015183313400181966954A 9121018Jan
1693015183313400181966954A 91111377247Jan
2693015183313400181966954A 90516361Jan
33365301131700171131856A 9121410Jan
43365301131700171131856A 9051260345Jan
53365301131700171131856A 90512588Jan
6174156154321378800881388591A 71111322143Jan
757232210120500511143580A 701213246407Jan
8572322101205181253511143580A 701215053Jan
9249252102251042502581001508981A 711111005199Jan

Last rows

TempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseAnzGesperrtFsEinzugRichtungLengthDurationMonth
3231753565542934131341431853799A 3111102822Dec
32327535655429340702431853799A 305163920Dec
323381612698236000861305511A 711158560Dec
323481612698236000861305511A 70518511Dec
323581612698236000861305511A 7-11130075Dec
3236601712219465400371671871A 905111611Dec
3237601712219465400371671871A 91213054Dec
3238601712219465400371671871A 90511291Dec
3239603895149080421862595A 960505225Dec
32403921221896700421003565A 73121151839Dec

Duplicate rows

Most frequent

TempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseAnzGesperrtFsEinzugRichtungLengthDurationMonthcount
471891621926157500851297505A 9311100Sep47
2836311014809092791286612A 312100Nov29
45177171845926910576321518738A 911100Sep20
60456459222616830576791667987A 911100Sep19
37111701903127200641673676A 9312100Aug8
583002612908246900881788737A 96111018Oct8
12151528311536140481555787A 911100Jul7
415152831153660481555787A 911100Jul6
10151528311536120481555787A 911100Jul6
18151528311536200481555787A 911100Jul6